SPECT + Deep Learning = Accurate Major Cardiac Event Prediction

Deep learning does a better job in predicting which patients will experience major adverse cardiac events than the current standard imaging protocols used to make these determinations, according to new research.

In a poster presented during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting, researchers from Cedars-Sinai Medical Center in Los Angeles created a novel deep learning algorithm that can give patients a personalized assessment of their annualized risk for these types of events, such as heart attack of death.

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“These findings show that artificial intelligence could be incorporated in standard clinical workstations to assist physicians in accurate and fast risk assessment of patients undergoing SPECT MPI scans,” said Ananya Singh, MS, a research software engineer in the Slomka Lab at Cedars-Sinai. “This work signifies the potential advantage of incorporating artificial intelligence techniques in standard imaging protocols to assist readers with risk stratification.”

Prediction performance of DL compared to quantitative measures and Kaplan-Meier curves for quartiles of DL.

Credit: Image created by Singh et al., Cedars-Sinai Medical Center, Los Angeles, CA.

To build their deep learning network, the team pulled data from 20,401 patients included in the largest available multi-center SPECT dataset – REgistry of Fast myocardial perfusion Imaging in NExt generation SPECT (REFINE SPECT). All patients had undergone SPECT MPI. The team used their network to score each patient on their individual likelihood to have a major adverse cardiac event during the average follow-up period of 4.7 years.

According to their analysis, the network was able to pinpoint the areas of the heart that were associated with an elevated risk of these events and return a risk score in less than 1 second. They determined that the patients with the highest deep learning scores had a 9.7-percent annual major cardiac event rate – a 10.2-fold increased risk over patients with the lowest scores.

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